metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
model-index:
- name: miosipof/whisper-tiny-ft-balbus-sep28k-v1.1
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Apple dataset
type: balbus-classifier
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7718583516139141
miosipof/whisper-tiny-ft-balbus-sep28k-v1.1
This model is a fine-tuned version of openai/whisper-small on the Apple dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.4870
- Accuracy: 0.7719
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6991 | 0.1253 | 100 | 0.6929 | 0.4616 |
| 0.686 | 0.2506 | 200 | 0.6816 | 0.5577 |
| 0.6776 | 0.3759 | 300 | 0.6726 | 0.5631 |
| 0.6591 | 0.5013 | 400 | 0.6472 | 0.6244 |
| 0.6317 | 0.6266 | 500 | 0.6115 | 0.6802 |
| 0.5836 | 0.7519 | 600 | 0.5672 | 0.7104 |
| 0.5415 | 0.8772 | 700 | 0.5192 | 0.7499 |
| 0.4856 | 1.0025 | 800 | 0.4999 | 0.7667 |
| 0.4886 | 1.1278 | 900 | 0.4894 | 0.7715 |
| 0.4727 | 1.2531 | 1000 | 0.4870 | 0.7719 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.21.0